Overview

Dataset statistics

Number of variables46
Number of observations7043
Missing cells15557
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory368.0 B

Variable types

Categorical11
Boolean17
Numeric18

Alerts

Customer ID has a high cardinality: 7043 distinct valuesHigh cardinality
City has a high cardinality: 1106 distinct valuesHigh cardinality
Number of Referrals is highly overall correlated with Referred a Friend and 1 other fieldsHigh correlation
Tenure in Months is highly overall correlated with Total Regular Charges and 2 other fieldsHigh correlation
Avg Monthly Long Distance Charges is highly overall correlated with Total Long Distance Charges and 1 other fieldsHigh correlation
Avg Monthly GB Download is highly overall correlated with Internet Service and 1 other fieldsHigh correlation
Monthly Charge is highly overall correlated with Total Regular Charges and 8 other fieldsHigh correlation
Total Regular Charges is highly overall correlated with Tenure in Months and 6 other fieldsHigh correlation
Total Long Distance Charges is highly overall correlated with Tenure in Months and 2 other fieldsHigh correlation
Age is highly overall correlated with Under 30 and 1 other fieldsHigh correlation
Number of Dependents is highly overall correlated with DependentsHigh correlation
Zip Code is highly overall correlated with Latitude and 1 other fieldsHigh correlation
Latitude is highly overall correlated with Zip Code and 1 other fieldsHigh correlation
Longitude is highly overall correlated with Zip Code and 1 other fieldsHigh correlation
Total Customer Svc Requests is highly overall correlated with Churn ValueHigh correlation
Referred a Friend is highly overall correlated with Number of Referrals and 1 other fieldsHigh correlation
Offer is highly overall correlated with Tenure in MonthsHigh correlation
Phone Service is highly overall correlated with Avg Monthly Long Distance Charges and 1 other fieldsHigh correlation
Multiple Lines is highly overall correlated with Monthly ChargeHigh correlation
Internet Service is highly overall correlated with Avg Monthly GB Download and 2 other fieldsHigh correlation
Internet Type is highly overall correlated with Monthly Charge and 1 other fieldsHigh correlation
Online Backup is highly overall correlated with Total Regular ChargesHigh correlation
Device Protection Plan is highly overall correlated with Monthly Charge and 1 other fieldsHigh correlation
Streaming TV is highly overall correlated with Monthly Charge and 2 other fieldsHigh correlation
Streaming Movies is highly overall correlated with Monthly Charge and 3 other fieldsHigh correlation
Streaming Music is highly overall correlated with Monthly Charge and 1 other fieldsHigh correlation
Under 30 is highly overall correlated with Avg Monthly GB Download and 1 other fieldsHigh correlation
Senior Citizen is highly overall correlated with AgeHigh correlation
Married is highly overall correlated with Number of Referrals and 1 other fieldsHigh correlation
Dependents is highly overall correlated with Number of DependentsHigh correlation
Churn Value is highly overall correlated with Total Customer Svc Requests and 3 other fieldsHigh correlation
Churn Category is highly overall correlated with Churn Value and 1 other fieldsHigh correlation
Churn Reason is highly overall correlated with Churn Value and 1 other fieldsHigh correlation
Customer Satisfaction is highly overall correlated with Churn ValueHigh correlation
Phone Service is highly imbalanced (54.1%)Imbalance
Churn Category has 5174 (73.5%) missing valuesMissing
Churn Reason has 5174 (73.5%) missing valuesMissing
Customer Satisfaction has 5209 (74.0%) missing valuesMissing
Customer ID is uniformly distributedUniform
Customer ID has unique valuesUnique
Number of Referrals has 3821 (54.3%) zerosZeros
Avg Monthly Long Distance Charges has 682 (9.7%) zerosZeros
Avg Monthly GB Download has 1526 (21.7%) zerosZeros
Total Refunds has 6518 (92.5%) zerosZeros
Total Extra Data Charges has 4011 (57.0%) zerosZeros
Total Long Distance Charges has 682 (9.7%) zerosZeros
Number of Dependents has 5416 (76.9%) zerosZeros
Total Customer Svc Requests has 2443 (34.7%) zerosZeros
Product/Service Issues Reported has 5621 (79.8%) zerosZeros

Reproduction

Analysis started2024-01-30 23:01:50.664213
Analysis finished2024-01-30 23:02:42.255344
Duration51.59 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Customer ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct7043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
8779-QRDMV
 
1
9817-APLHW
 
1
5447-VYTKW
 
1
8871-JLMHM
 
1
7029-IJEJK
 
1
Other values (7038)
7038 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7043 ?
Unique (%)100.0%

Sample

1st row8779-QRDMV
2nd row7495-OOKFY
3rd row1658-BYGOY
4th row4598-XLKNJ
5th row4846-WHAFZ

Common Values

ValueCountFrequency (%)
8779-QRDMV 1
 
< 0.1%
9817-APLHW 1
 
< 0.1%
5447-VYTKW 1
 
< 0.1%
8871-JLMHM 1
 
< 0.1%
7029-IJEJK 1
 
< 0.1%
4536-PLEQY 1
 
< 0.1%
6923-AQONU 1
 
< 0.1%
1335-HQMKX 1
 
< 0.1%
4616-EWBNJ 1
 
< 0.1%
0384-LPITE 1
 
< 0.1%
Other values (7033) 7033
99.9%

Length

2024-01-30T15:02:42.349466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8779-qrdmv 1
 
< 0.1%
7273-tefqd 1
 
< 0.1%
4598-xlknj 1
 
< 0.1%
4846-whafz 1
 
< 0.1%
4412-yltkf 1
 
< 0.1%
0390-dcfdq 1
 
< 0.1%
3445-hxxgf 1
 
< 0.1%
2656-fmokz 1
 
< 0.1%
2070-fnexe 1
 
< 0.1%
0094-oifmo 1
 
< 0.1%
Other values (7033) 7033
99.9%

Most occurring characters

ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 35215
50.0%
Decimal Number 28172
40.0%
Dash Punctuation 7043
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1442
 
4.1%
H 1396
 
4.0%
B 1393
 
4.0%
S 1386
 
3.9%
V 1382
 
3.9%
T 1374
 
3.9%
C 1368
 
3.9%
Z 1368
 
3.9%
K 1363
 
3.9%
L 1363
 
3.9%
Other values (16) 21380
60.7%
Decimal Number
ValueCountFrequency (%)
2 2901
10.3%
9 2881
10.2%
6 2870
10.2%
7 2836
10.1%
0 2831
10.0%
8 2812
10.0%
5 2810
10.0%
3 2791
9.9%
1 2726
9.7%
4 2714
9.6%
Dash Punctuation
ValueCountFrequency (%)
- 7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35215
50.0%
Latin 35215
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1442
 
4.1%
H 1396
 
4.0%
B 1393
 
4.0%
S 1386
 
3.9%
V 1382
 
3.9%
T 1374
 
3.9%
C 1368
 
3.9%
Z 1368
 
3.9%
K 1363
 
3.9%
L 1363
 
3.9%
Other values (16) 21380
60.7%
Common
ValueCountFrequency (%)
- 7043
20.0%
2 2901
8.2%
9 2881
8.2%
6 2870
8.1%
7 2836
8.1%
0 2831
8.0%
8 2812
 
8.0%
5 2810
 
8.0%
3 2791
 
7.9%
1 2726
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3821 
True
3222 
ValueCountFrequency (%)
False 3821
54.3%
True 3222
45.7%
2024-01-30T15:02:42.490447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Number of Referrals
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9518671
Minimum0
Maximum11
Zeros3821
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:42.584567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0011993
Coefficient of variation (CV)1.5376043
Kurtosis0.72196393
Mean1.9518671
Median Absolute Deviation (MAD)0
Skewness1.4460596
Sum13747
Variance9.0071972
MonotonicityNot monotonic
2024-01-30T15:02:42.663060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 3821
54.3%
1 1086
 
15.4%
5 264
 
3.7%
3 255
 
3.6%
7 248
 
3.5%
9 238
 
3.4%
2 236
 
3.4%
4 236
 
3.4%
10 223
 
3.2%
6 221
 
3.1%
Other values (2) 215
 
3.1%
ValueCountFrequency (%)
0 3821
54.3%
1 1086
 
15.4%
2 236
 
3.4%
3 255
 
3.6%
4 236
 
3.4%
5 264
 
3.7%
6 221
 
3.1%
7 248
 
3.5%
8 213
 
3.0%
9 238
 
3.4%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 223
3.2%
9 238
3.4%
8 213
3.0%
7 248
3.5%
6 221
3.1%
5 264
3.7%
4 236
3.4%
3 255
3.6%
2 236
3.4%

Tenure in Months
Real number (ℝ)

Distinct72
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.386767
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:42.819668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range71
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.542061
Coefficient of variation (CV)0.75778052
Kurtosis-1.3870524
Mean32.386767
Median Absolute Deviation (MAD)22
Skewness0.24054261
Sum228100
Variance602.31276
MonotonicityNot monotonic
2024-01-30T15:02:42.961141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 613
 
8.7%
72 362
 
5.1%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
71 170
 
2.4%
5 133
 
1.9%
7 131
 
1.9%
10 127
 
1.8%
8 123
 
1.7%
Other values (62) 4770
67.7%
ValueCountFrequency (%)
1 613
8.7%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
5 133
 
1.9%
6 110
 
1.6%
7 131
 
1.9%
8 123
 
1.7%
9 119
 
1.7%
10 127
 
1.8%
ValueCountFrequency (%)
72 362
5.1%
71 170
2.4%
70 119
 
1.7%
69 95
 
1.3%
68 100
 
1.4%
67 98
 
1.4%
66 89
 
1.3%
65 76
 
1.1%
64 80
 
1.1%
63 72
 
1.0%

Offer
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
None
3877 
Offer B
824 
Offer E
805 
Offer D
602 
Offer A
520 

Length

Max length7
Median length4
Mean length5.3485731
Min length4

Characters and Unicode

Total characters37670
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowOffer E
3rd rowOffer D
4th rowOffer C
5th rowOffer C

Common Values

ValueCountFrequency (%)
None 3877
55.0%
Offer B 824
 
11.7%
Offer E 805
 
11.4%
Offer D 602
 
8.5%
Offer A 520
 
7.4%
Offer C 415
 
5.9%

Length

2024-01-30T15:02:43.102194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T15:02:43.231199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
none 3877
38.0%
offer 3166
31.0%
b 824
 
8.1%
e 805
 
7.9%
d 602
 
5.9%
a 520
 
5.1%
c 415
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e 7043
18.7%
f 6332
16.8%
N 3877
10.3%
o 3877
10.3%
n 3877
10.3%
O 3166
8.4%
r 3166
8.4%
3166
8.4%
B 824
 
2.2%
E 805
 
2.1%
Other values (3) 1537
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24295
64.5%
Uppercase Letter 10209
27.1%
Space Separator 3166
 
8.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 3877
38.0%
O 3166
31.0%
B 824
 
8.1%
E 805
 
7.9%
D 602
 
5.9%
A 520
 
5.1%
C 415
 
4.1%
Lowercase Letter
ValueCountFrequency (%)
e 7043
29.0%
f 6332
26.1%
o 3877
16.0%
n 3877
16.0%
r 3166
13.0%
Space Separator
ValueCountFrequency (%)
3166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34504
91.6%
Common 3166
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7043
20.4%
f 6332
18.4%
N 3877
11.2%
o 3877
11.2%
n 3877
11.2%
O 3166
9.2%
r 3166
9.2%
B 824
 
2.4%
E 805
 
2.3%
D 602
 
1.7%
Other values (2) 935
 
2.7%
Common
ValueCountFrequency (%)
3166
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7043
18.7%
f 6332
16.8%
N 3877
10.3%
o 3877
10.3%
n 3877
10.3%
O 3166
8.4%
r 3166
8.4%
3166
8.4%
B 824
 
2.2%
E 805
 
2.1%
Other values (3) 1537
 
4.1%

Phone Service
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
6361 
False
682 
ValueCountFrequency (%)
True 6361
90.3%
False 682
 
9.7%
2024-01-30T15:02:43.384729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Avg Monthly Long Distance Charges
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3584
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.958954
Minimum0
Maximum49.99
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:43.493972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.21
median22.89
Q336.395
95-th percentile47.34
Maximum49.99
Range49.99
Interquartile range (IQR)27.185

Descriptive statistics

Standard deviation15.448113
Coefficient of variation (CV)0.6728579
Kurtosis-1.2546544
Mean22.958954
Median Absolute Deviation (MAD)13.6
Skewness0.049175899
Sum161699.91
Variance238.64421
MonotonicityNot monotonic
2024-01-30T15:02:43.651098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
 
9.7%
18.26 7
 
0.1%
18.74 6
 
0.1%
30.09 6
 
0.1%
42.55 6
 
0.1%
45.92 6
 
0.1%
30.07 6
 
0.1%
25.57 6
 
0.1%
49.51 6
 
0.1%
18.42 6
 
0.1%
Other values (3574) 6306
89.5%
ValueCountFrequency (%)
0 682
9.7%
1.01 1
 
< 0.1%
1.02 3
 
< 0.1%
1.03 1
 
< 0.1%
1.05 1
 
< 0.1%
1.06 1
 
< 0.1%
1.07 1
 
< 0.1%
1.08 2
 
< 0.1%
1.09 2
 
< 0.1%
1.1 1
 
< 0.1%
ValueCountFrequency (%)
49.99 1
 
< 0.1%
49.98 3
< 0.1%
49.96 2
< 0.1%
49.95 2
< 0.1%
49.94 1
 
< 0.1%
49.92 1
 
< 0.1%
49.91 3
< 0.1%
49.9 3
< 0.1%
49.88 1
 
< 0.1%
49.87 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4072 
True
2971 
ValueCountFrequency (%)
False 4072
57.8%
True 2971
42.2%
2024-01-30T15:02:43.776467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
5517 
False
1526 
ValueCountFrequency (%)
True 5517
78.3%
False 1526
 
21.7%
2024-01-30T15:02:43.901882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Internet Type
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Fiber Optic
2291 
DSL
1680 
Cable
1546 
None
1526 

Length

Max length11
Median length5
Mean length6.2579867
Min length3

Characters and Unicode

Total characters44075
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFiber Optic
2nd rowCable
3rd rowFiber Optic
4th rowFiber Optic
5th rowCable

Common Values

ValueCountFrequency (%)
Fiber Optic 2291
32.5%
DSL 1680
23.9%
Cable 1546
22.0%
None 1526
21.7%

Length

2024-01-30T15:02:44.027322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T15:02:44.184321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
fiber 2291
24.5%
optic 2291
24.5%
dsl 1680
18.0%
cable 1546
16.6%
none 1526
16.3%

Most occurring characters

ValueCountFrequency (%)
e 5363
 
12.2%
i 4582
 
10.4%
b 3837
 
8.7%
F 2291
 
5.2%
p 2291
 
5.2%
t 2291
 
5.2%
c 2291
 
5.2%
O 2291
 
5.2%
2291
 
5.2%
r 2291
 
5.2%
Other values (9) 14256
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29090
66.0%
Uppercase Letter 12694
28.8%
Space Separator 2291
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5363
18.4%
i 4582
15.8%
b 3837
13.2%
p 2291
7.9%
t 2291
7.9%
c 2291
7.9%
r 2291
7.9%
a 1546
 
5.3%
l 1546
 
5.3%
o 1526
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
F 2291
18.0%
O 2291
18.0%
D 1680
13.2%
S 1680
13.2%
L 1680
13.2%
C 1546
12.2%
N 1526
12.0%
Space Separator
ValueCountFrequency (%)
2291
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41784
94.8%
Common 2291
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5363
12.8%
i 4582
 
11.0%
b 3837
 
9.2%
F 2291
 
5.5%
p 2291
 
5.5%
t 2291
 
5.5%
c 2291
 
5.5%
O 2291
 
5.5%
r 2291
 
5.5%
D 1680
 
4.0%
Other values (8) 12576
30.1%
Common
ValueCountFrequency (%)
2291
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5363
 
12.2%
i 4582
 
10.4%
b 3837
 
8.7%
F 2291
 
5.2%
p 2291
 
5.2%
t 2291
 
5.2%
c 2291
 
5.2%
O 2291
 
5.2%
2291
 
5.2%
r 2291
 
5.2%
Other values (9) 14256
32.3%

Avg Monthly GB Download
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.11089
Minimum0
Maximum94
Zeros1526
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:44.325300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median17
Q328
95-th percentile69
Maximum94
Range94
Interquartile range (IQR)25

Descriptive statistics

Standard deviation20.948471
Coefficient of variation (CV)0.9923064
Kurtosis0.89649271
Mean21.11089
Median Absolute Deviation (MAD)12
Skewness1.2112174
Sum148684
Variance438.83845
MonotonicityNot monotonic
2024-01-30T15:02:44.466667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1526
 
21.7%
19 208
 
3.0%
30 203
 
2.9%
23 175
 
2.5%
21 172
 
2.4%
13 171
 
2.4%
24 164
 
2.3%
22 162
 
2.3%
26 162
 
2.3%
29 162
 
2.3%
Other values (53) 3938
55.9%
ValueCountFrequency (%)
0 1526
21.7%
2 116
 
1.6%
3 130
 
1.8%
4 129
 
1.8%
5 68
 
1.0%
6 113
 
1.6%
7 119
 
1.7%
8 122
 
1.7%
9 114
 
1.6%
10 132
 
1.9%
ValueCountFrequency (%)
94 13
 
0.2%
90 8
 
0.1%
85 35
0.5%
84 13
 
0.2%
82 35
0.5%
80 18
 
0.3%
78 13
 
0.2%
76 64
0.9%
75 15
 
0.2%
73 63
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5024 
True
2019 
ValueCountFrequency (%)
False 5024
71.3%
True 2019
28.7%
2024-01-30T15:02:44.623336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4614 
True
2429 
ValueCountFrequency (%)
False 4614
65.5%
True 2429
34.5%
2024-01-30T15:02:44.764781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4621 
True
2422 
ValueCountFrequency (%)
False 4621
65.6%
True 2422
34.4%
2024-01-30T15:02:44.890240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4999 
True
2044 
ValueCountFrequency (%)
False 4999
71.0%
True 2044
29.0%
2024-01-30T15:02:45.000078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4336 
True
2707 
ValueCountFrequency (%)
False 4336
61.6%
True 2707
38.4%
2024-01-30T15:02:45.109828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4311 
True
2732 
ValueCountFrequency (%)
False 4311
61.2%
True 2732
38.8%
2024-01-30T15:02:45.219636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4555 
True
2488 
ValueCountFrequency (%)
False 4555
64.7%
True 2488
35.3%
2024-01-30T15:02:45.335381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4339 
True
2704 
ValueCountFrequency (%)
False 4339
61.6%
True 2704
38.4%
2024-01-30T15:02:45.454633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Contract
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Month-to-Month
3610 
Two Year
1883 
One Year
1550 

Length

Max length14
Median length14
Mean length11.075394
Min length8

Characters and Unicode

Total characters78004
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonth-to-Month
2nd rowMonth-to-Month
3rd rowMonth-to-Month
4th rowMonth-to-Month
5th rowMonth-to-Month

Common Values

ValueCountFrequency (%)
Month-to-Month 3610
51.3%
Two Year 1883
26.7%
One Year 1550
22.0%

Length

2024-01-30T15:02:45.564443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T15:02:45.689807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month 3610
34.5%
year 3433
32.8%
two 1883
18.0%
one 1550
14.8%

Most occurring characters

ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53265
68.3%
Uppercase Letter 14086
 
18.1%
Dash Punctuation 7220
 
9.3%
Space Separator 3433
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 12713
23.9%
t 10830
20.3%
n 8770
16.5%
h 7220
13.6%
e 4983
 
9.4%
a 3433
 
6.4%
r 3433
 
6.4%
w 1883
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
M 7220
51.3%
Y 3433
24.4%
T 1883
 
13.4%
O 1550
 
11.0%
Dash Punctuation
ValueCountFrequency (%)
- 7220
100.0%
Space Separator
ValueCountFrequency (%)
3433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67351
86.3%
Common 10653
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 12713
18.9%
t 10830
16.1%
n 8770
13.0%
M 7220
10.7%
h 7220
10.7%
e 4983
 
7.4%
Y 3433
 
5.1%
a 3433
 
5.1%
r 3433
 
5.1%
T 1883
 
2.8%
Other values (2) 3433
 
5.1%
Common
ValueCountFrequency (%)
- 7220
67.8%
3433
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4171 
False
2872 
ValueCountFrequency (%)
True 4171
59.2%
False 2872
40.8%
2024-01-30T15:02:45.836303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Payment Method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Bank Withdrawal
3909 
Credit Card
2749 
Mailed Check
 
385

Length

Max length15
Median length15
Mean length13.274741
Min length11

Characters and Unicode

Total characters93494
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBank Withdrawal
2nd rowCredit Card
3rd rowBank Withdrawal
4th rowBank Withdrawal
5th rowBank Withdrawal

Common Values

ValueCountFrequency (%)
Bank Withdrawal 3909
55.5%
Credit Card 2749
39.0%
Mailed Check 385
 
5.5%

Length

2024-01-30T15:02:45.944248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T15:02:46.085296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
bank 3909
27.8%
withdrawal 3909
27.8%
credit 2749
19.5%
card 2749
19.5%
mailed 385
 
2.7%
check 385
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72365
77.4%
Uppercase Letter 14086
 
15.1%
Space Separator 7043
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14861
20.5%
d 9792
13.5%
r 9407
13.0%
i 7043
9.7%
t 6658
9.2%
h 4294
 
5.9%
k 4294
 
5.9%
l 4294
 
5.9%
w 3909
 
5.4%
n 3909
 
5.4%
Other values (2) 3904
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
C 5883
41.8%
B 3909
27.8%
W 3909
27.8%
M 385
 
2.7%
Space Separator
ValueCountFrequency (%)
7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86451
92.5%
Common 7043
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14861
17.2%
d 9792
11.3%
r 9407
10.9%
i 7043
8.1%
t 6658
7.7%
C 5883
 
6.8%
h 4294
 
5.0%
k 4294
 
5.0%
l 4294
 
5.0%
w 3909
 
4.5%
Other values (6) 16016
18.5%
Common
ValueCountFrequency (%)
7043
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Monthly Charge
Real number (ℝ)

Distinct2298
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.5388
Minimum18.25
Maximum123.084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:46.226288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.89
median71.968
Q390.65
95-th percentile109.0494
Maximum123.084
Range104.834
Interquartile range (IQR)54.76

Descriptive statistics

Standard deviation30.606805
Coefficient of variation (CV)0.46700283
Kurtosis-1.2654401
Mean65.5388
Median Absolute Deviation (MAD)23.432
Skewness-0.22290146
Sum461589.77
Variance936.77653
MonotonicityNot monotonic
2024-01-30T15:02:46.367672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.05 61
 
0.9%
19.85 45
 
0.6%
19.95 44
 
0.6%
19.9 44
 
0.6%
19.7 43
 
0.6%
19.65 43
 
0.6%
20 43
 
0.6%
20.15 40
 
0.6%
19.55 40
 
0.6%
19.75 39
 
0.6%
Other values (2288) 6601
93.7%
ValueCountFrequency (%)
18.25 1
 
< 0.1%
18.4 1
 
< 0.1%
18.55 1
 
< 0.1%
18.7 2
 
< 0.1%
18.75 1
 
< 0.1%
18.8 7
0.1%
18.85 5
0.1%
18.9 2
 
< 0.1%
18.95 6
0.1%
19 7
0.1%
ValueCountFrequency (%)
123.084 1
< 0.1%
122.512 1
< 0.1%
122.148 1
< 0.1%
120.848 1
< 0.1%
120.276 1
< 0.1%
120.172 1
< 0.1%
119.08 1
< 0.1%
118.768 1
< 0.1%
118.75 1
< 0.1%
118.65 1
< 0.1%

Total Regular Charges
Real number (ℝ)

Distinct6540
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2280.3813
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:46.524280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.65
Q1400.15
median1394.55
Q33786.6
95-th percentile6921.025
Maximum8684.8
Range8666
Interquartile range (IQR)3386.45

Descriptive statistics

Standard deviation2266.2205
Coefficient of variation (CV)0.99379016
Kurtosis-0.22769266
Mean2280.3813
Median Absolute Deviation (MAD)1219.75
Skewness0.96379109
Sum16060725
Variance5135755.2
MonotonicityNot monotonic
2024-01-30T15:02:46.650033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.2 11
 
0.2%
19.75 9
 
0.1%
19.9 8
 
0.1%
19.65 8
 
0.1%
20.05 8
 
0.1%
45.3 7
 
0.1%
19.55 7
 
0.1%
20.15 6
 
0.1%
19.45 6
 
0.1%
20.25 6
 
0.1%
Other values (6530) 6967
98.9%
ValueCountFrequency (%)
18.8 1
 
< 0.1%
18.85 2
< 0.1%
18.9 1
 
< 0.1%
19 1
 
< 0.1%
19.05 1
 
< 0.1%
19.1 3
< 0.1%
19.15 1
 
< 0.1%
19.2 4
0.1%
19.25 3
< 0.1%
19.3 4
0.1%
ValueCountFrequency (%)
8684.8 1
< 0.1%
8672.45 1
< 0.1%
8670.1 1
< 0.1%
8594.4 1
< 0.1%
8564.75 1
< 0.1%
8547.15 1
< 0.1%
8543.25 1
< 0.1%
8529.5 1
< 0.1%
8496.7 1
< 0.1%
8477.7 1
< 0.1%

Total Refunds
Real number (ℝ)

Distinct500
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9621823
Minimum0
Maximum49.79
Zeros6518
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:46.791026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18.149
Maximum49.79
Range49.79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9026144
Coefficient of variation (CV)4.0274618
Kurtosis18.350658
Mean1.9621823
Median Absolute Deviation (MAD)0
Skewness4.3285167
Sum13819.65
Variance62.451314
MonotonicityNot monotonic
2024-01-30T15:02:46.947620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6518
92.5%
46.06 2
 
< 0.1%
9.73 2
 
< 0.1%
32.55 2
 
< 0.1%
27.6 2
 
< 0.1%
41.74 2
 
< 0.1%
12.48 2
 
< 0.1%
29.76 2
 
< 0.1%
25.67 2
 
< 0.1%
29.88 2
 
< 0.1%
Other values (490) 507
 
7.2%
ValueCountFrequency (%)
0 6518
92.5%
1.01 1
 
< 0.1%
1.09 1
 
< 0.1%
1.27 1
 
< 0.1%
1.31 2
 
< 0.1%
1.48 1
 
< 0.1%
1.65 1
 
< 0.1%
1.66 1
 
< 0.1%
1.69 1
 
< 0.1%
1.83 1
 
< 0.1%
ValueCountFrequency (%)
49.79 1
< 0.1%
49.76 1
< 0.1%
49.57 2
< 0.1%
49.53 1
< 0.1%
49.51 1
< 0.1%
49.38 1
< 0.1%
49.37 1
< 0.1%
49.24 1
< 0.1%
49.23 1
< 0.1%
49.22 1
< 0.1%

Total Extra Data Charges
Real number (ℝ)

Distinct1798
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278.49922
Minimum0
Maximum6477
Zeros4011
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:47.088705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3182.62
95-th percentile1541.8
Maximum6477
Range6477
Interquartile range (IQR)182.62

Descriptive statistics

Standard deviation685.03963
Coefficient of variation (CV)2.4597542
Kurtosis20.930331
Mean278.49922
Median Absolute Deviation (MAD)0
Skewness4.0956178
Sum1961470
Variance469279.29
MonotonicityNot monotonic
2024-01-30T15:02:47.245741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4011
57.0%
34 13
 
0.2%
38 12
 
0.2%
40 11
 
0.2%
35 10
 
0.1%
83 10
 
0.1%
25 10
 
0.1%
69 10
 
0.1%
26 10
 
0.1%
49 9
 
0.1%
Other values (1788) 2937
41.7%
ValueCountFrequency (%)
0 4011
57.0%
0.38 1
 
< 0.1%
0.59 1
 
< 0.1%
0.72 1
 
< 0.1%
0.93 1
 
< 0.1%
0.95 1
 
< 0.1%
1.37 1
 
< 0.1%
1.68 1
 
< 0.1%
1.99 1
 
< 0.1%
2 4
 
0.1%
ValueCountFrequency (%)
6477 1
< 0.1%
6431 1
< 0.1%
6318 1
< 0.1%
6261 1
< 0.1%
6078 1
< 0.1%
6047 1
< 0.1%
5882 1
< 0.1%
5788 1
< 0.1%
5741 1
< 0.1%
5602 1
< 0.1%

Total Long Distance Charges
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6087
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.09926
Minimum0
Maximum3564.72
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:47.417978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170.545
median401.44
Q31191.1
95-th percentile2577.877
Maximum3564.72
Range3564.72
Interquartile range (IQR)1120.555

Descriptive statistics

Standard deviation846.66005
Coefficient of variation (CV)1.1302375
Kurtosis0.64409208
Mean749.09926
Median Absolute Deviation (MAD)382.12
Skewness1.238282
Sum5275906.1
Variance716833.25
MonotonicityNot monotonic
2024-01-30T15:02:47.559355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
 
9.7%
22.86 4
 
0.1%
15.6 4
 
0.1%
48.96 4
 
0.1%
2077.92 3
 
< 0.1%
177.12 3
 
< 0.1%
26 3
 
< 0.1%
597.6 3
 
< 0.1%
24.48 3
 
< 0.1%
378 3
 
< 0.1%
Other values (6077) 6331
89.9%
ValueCountFrequency (%)
0 682
9.7%
1.13 1
 
< 0.1%
1.15 1
 
< 0.1%
1.17 1
 
< 0.1%
1.23 1
 
< 0.1%
1.28 1
 
< 0.1%
1.47 1
 
< 0.1%
1.48 1
 
< 0.1%
1.5 1
 
< 0.1%
1.59 1
 
< 0.1%
ValueCountFrequency (%)
3564.72 1
< 0.1%
3564 1
< 0.1%
3536.64 1
< 0.1%
3515.92 1
< 0.1%
3508.82 1
< 0.1%
3501.72 1
< 0.1%
3493.44 1
< 0.1%
3492.72 1
< 0.1%
3487.68 1
< 0.1%
3482.64 1
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Male
3555 
Female
3488 

Length

Max length6
Median length4
Mean length4.990487
Min length4

Characters and Unicode

Total characters35148
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 3555
50.5%
Female 3488
49.5%

Length

2024-01-30T15:02:47.716050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T15:02:47.841793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
male 3555
50.5%
female 3488
49.5%

Most occurring characters

ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28105
80.0%
Uppercase Letter 7043
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10531
37.5%
a 7043
25.1%
l 7043
25.1%
m 3488
 
12.4%
Uppercase Letter
ValueCountFrequency (%)
M 3555
50.5%
F 3488
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 35148
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Age
Real number (ℝ)

Distinct62
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.509726
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:47.951600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21
Q132
median46
Q360
95-th percentile75
Maximum80
Range61
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.750352
Coefficient of variation (CV)0.36014729
Kurtosis-1.0028495
Mean46.509726
Median Absolute Deviation (MAD)14
Skewness0.16218645
Sum327568
Variance280.57428
MonotonicityNot monotonic
2024-01-30T15:02:48.076998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 156
 
2.2%
47 153
 
2.2%
40 150
 
2.1%
44 148
 
2.1%
23 146
 
2.1%
56 144
 
2.0%
62 143
 
2.0%
35 142
 
2.0%
21 140
 
2.0%
33 139
 
2.0%
Other values (52) 5582
79.3%
ValueCountFrequency (%)
19 127
1.8%
20 127
1.8%
21 140
2.0%
22 130
1.8%
23 146
2.1%
24 109
1.5%
25 138
2.0%
26 115
1.6%
27 132
1.9%
28 119
1.7%
ValueCountFrequency (%)
80 66
0.9%
79 76
1.1%
78 63
0.9%
77 72
1.0%
76 69
1.0%
75 74
1.1%
74 76
1.1%
73 85
1.2%
72 58
0.8%
71 68
1.0%

Under 30
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5642 
True
1401 
ValueCountFrequency (%)
False 5642
80.1%
True 1401
 
19.9%
2024-01-30T15:02:48.217950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5901 
True
1142 
ValueCountFrequency (%)
False 5901
83.8%
True 1142
 
16.2%
2024-01-30T15:02:48.312077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Married
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3641 
True
3402 
ValueCountFrequency (%)
False 3641
51.7%
True 3402
48.3%
2024-01-30T15:02:48.440980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Dependents
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5416 
True
1627 
ValueCountFrequency (%)
False 5416
76.9%
True 1627
 
23.1%
2024-01-30T15:02:48.547637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Number of Dependents
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46869232
Minimum0
Maximum9
Zeros5416
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:48.642156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96280195
Coefficient of variation (CV)2.0542303
Kurtosis4.4463579
Mean0.46869232
Median Absolute Deviation (MAD)0
Skewness2.109932
Sum3301
Variance0.9269876
MonotonicityNot monotonic
2024-01-30T15:02:48.751967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 5416
76.9%
1 553
 
7.9%
2 531
 
7.5%
3 517
 
7.3%
5 10
 
0.1%
4 9
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 5416
76.9%
1 553
 
7.9%
2 531
 
7.5%
3 517
 
7.3%
4 9
 
0.1%
5 10
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 3
 
< 0.1%
5 10
 
0.1%
4 9
 
0.1%
3 517
 
7.3%
2 531
 
7.5%
1 553
 
7.9%
0 5416
76.9%

City
Categorical

Distinct1106
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Los Angeles
 
293
San Diego
 
285
San Jose
 
112
Sacramento
 
108
San Francisco
 
104
Other values (1101)
6141 

Length

Max length22
Median length19
Mean length9.2034644
Min length3

Characters and Unicode

Total characters64820
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLos Angeles
2nd rowLos Angeles
3rd rowLos Angeles
4th rowInglewood
5th rowWhittier

Common Values

ValueCountFrequency (%)
Los Angeles 293
 
4.2%
San Diego 285
 
4.0%
San Jose 112
 
1.6%
Sacramento 108
 
1.5%
San Francisco 104
 
1.5%
Fresno 61
 
0.9%
Long Beach 60
 
0.9%
Oakland 52
 
0.7%
Escondido 51
 
0.7%
Stockton 44
 
0.6%
Other values (1096) 5873
83.4%

Length

2024-01-30T15:02:48.893022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 718
 
6.9%
los 337
 
3.3%
angeles 293
 
2.8%
diego 285
 
2.8%
santa 181
 
1.8%
valley 171
 
1.7%
beach 169
 
1.6%
city 150
 
1.5%
sacramento 116
 
1.1%
jose 112
 
1.1%
Other values (1110) 7807
75.5%

Most occurring characters

ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51185
79.0%
Uppercase Letter 10339
 
16.0%
Space Separator 3296
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6946
13.6%
e 6111
11.9%
n 5134
10.0%
o 5074
9.9%
l 3970
7.8%
r 3568
 
7.0%
i 3423
 
6.7%
s 2853
 
5.6%
t 2602
 
5.1%
d 1669
 
3.3%
Other values (16) 9835
19.2%
Uppercase Letter
ValueCountFrequency (%)
S 1576
15.2%
C 977
 
9.4%
L 869
 
8.4%
B 731
 
7.1%
A 651
 
6.3%
M 599
 
5.8%
P 582
 
5.6%
D 533
 
5.2%
F 471
 
4.6%
R 447
 
4.3%
Other values (15) 2903
28.1%
Space Separator
ValueCountFrequency (%)
3296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61524
94.9%
Common 3296
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6946
 
11.3%
e 6111
 
9.9%
n 5134
 
8.3%
o 5074
 
8.2%
l 3970
 
6.5%
r 3568
 
5.8%
i 3423
 
5.6%
s 2853
 
4.6%
t 2602
 
4.2%
d 1669
 
2.7%
Other values (41) 20174
32.8%
Common
ValueCountFrequency (%)
3296
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Zip Code
Real number (ℝ)

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93486.071
Minimum90001
Maximum96150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:49.050133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum90001
5-th percentile90241.1
Q192101
median93518
Q395329
95-th percentile96020.9
Maximum96150
Range6149
Interquartile range (IQR)3228

Descriptive statistics

Standard deviation1856.7675
Coefficient of variation (CV)0.019861435
Kurtosis-1.1739154
Mean93486.071
Median Absolute Deviation (MAD)1605
Skewness-0.20961512
Sum6.584224 × 108
Variance3447585.6
MonotonicityNot monotonic
2024-01-30T15:02:49.191244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92028 43
 
0.6%
92027 38
 
0.5%
92122 36
 
0.5%
92117 34
 
0.5%
92126 32
 
0.5%
92592 30
 
0.4%
92109 27
 
0.4%
92130 22
 
0.3%
92121 20
 
0.3%
92129 16
 
0.2%
Other values (1616) 6745
95.8%
ValueCountFrequency (%)
90001 4
0.1%
90002 4
0.1%
90003 5
0.1%
90004 5
0.1%
90005 4
0.1%
90006 5
0.1%
90007 5
0.1%
90008 5
0.1%
90010 4
0.1%
90011 5
0.1%
ValueCountFrequency (%)
96150 2
< 0.1%
96148 4
0.1%
96146 4
0.1%
96145 3
< 0.1%
96143 4
0.1%
96142 3
< 0.1%
96141 3
< 0.1%
96140 4
0.1%
96137 4
0.1%
96136 4
0.1%

Latitude
Real number (ℝ)

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.197455
Minimum32.555828
Maximum41.962127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:49.567871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum32.555828
5-th percentile32.886925
Q133.990646
median36.205465
Q338.161321
95-th percentile40.497425
Maximum41.962127
Range9.406299
Interquartile range (IQR)4.170675

Descriptive statistics

Standard deviation2.4689287
Coefficient of variation (CV)0.068207245
Kurtosis-1.1605061
Mean36.197455
Median Absolute Deviation (MAD)2.169863
Skewness0.31480427
Sum254938.67
Variance6.0956088
MonotonicityNot monotonic
2024-01-30T15:02:49.708860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.362575 43
 
0.6%
33.141265 38
 
0.5%
32.85723 36
 
0.5%
32.825086 34
 
0.5%
32.886925 32
 
0.5%
33.507255 30
 
0.4%
32.787836 27
 
0.4%
32.957195 22
 
0.3%
32.898613 20
 
0.3%
32.961064 16
 
0.2%
Other values (1616) 6745
95.8%
ValueCountFrequency (%)
32.555828 5
0.1%
32.578103 4
0.1%
32.579134 4
0.1%
32.587557 5
0.1%
32.605012 4
0.1%
32.607964 5
0.1%
32.619465 5
0.1%
32.622999 4
0.1%
32.636792 4
0.1%
32.64164 5
0.1%
ValueCountFrequency (%)
41.962127 4
0.1%
41.950683 4
0.1%
41.949216 4
0.1%
41.932207 3
< 0.1%
41.924174 3
< 0.1%
41.867908 4
0.1%
41.831901 4
0.1%
41.816595 4
0.1%
41.813521 4
0.1%
41.769709 4
0.1%

Longitude
Real number (ℝ)

Distinct1625
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.75668
Minimum-124.30137
Maximum-114.1929
Zeros0
Zeros (%)0.0%
Negative7043
Negative (%)100.0%
Memory size55.2 KiB
2024-01-30T15:02:49.865866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-124.30137
5-th percentile-122.9755
Q1-121.78809
median-119.59529
Q3-117.9698
95-th percentile-116.87326
Maximum-114.1929
Range10.108471
Interquartile range (IQR)3.818295

Descriptive statistics

Standard deviation2.1544251
Coefficient of variation (CV)-0.01799002
Kurtosis-1.1912906
Mean-119.75668
Median Absolute Deviation (MAD)1.848851
Skewness-0.091931635
Sum-843446.32
Variance4.6415475
MonotonicityNot monotonic
2024-01-30T15:02:50.006346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-117.299644 43
 
0.6%
-116.967221 38
 
0.5%
-117.209774 36
 
0.5%
-117.199424 34
 
0.5%
-117.152162 32
 
0.5%
-117.029473 30
 
0.4%
-117.232376 27
 
0.4%
-117.202542 22
 
0.3%
-117.202937 20
 
0.3%
-117.134917 16
 
0.2%
Other values (1615) 6745
95.8%
ValueCountFrequency (%)
-124.301372 4
0.1%
-124.240051 4
0.1%
-124.217378 4
0.1%
-124.210902 4
0.1%
-124.189977 4
0.1%
-124.163234 4
0.1%
-124.15428 4
0.1%
-124.121504 4
0.1%
-124.108897 4
0.1%
-124.098739 4
0.1%
ValueCountFrequency (%)
-114.192901 4
0.1%
-114.36514 5
0.1%
-114.702256 4
0.1%
-114.71612 4
0.1%
-114.758334 5
0.1%
-114.850784 4
0.1%
-115.152865 2
 
< 0.1%
-115.191857 5
0.1%
-115.257009 5
0.1%
-115.287901 4
0.1%

Population
Real number (ℝ)

Distinct1569
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22139.603
Minimum11
Maximum105285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:50.147723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile227
Q12344
median17554
Q336125
95-th percentile62065.7
Maximum105285
Range105274
Interquartile range (IQR)33781

Descriptive statistics

Standard deviation21152.393
Coefficient of variation (CV)0.95540975
Kurtosis0.32546021
Mean22139.603
Median Absolute Deviation (MAD)15831
Skewness0.91285402
Sum1.5592923 × 108
Variance4.4742372 × 108
MonotonicityNot monotonic
2024-01-30T15:02:50.304430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42239 43
 
0.6%
48690 38
 
0.5%
34902 36
 
0.5%
51213 34
 
0.5%
74232 32
 
0.5%
46171 30
 
0.4%
46086 27
 
0.4%
28201 22
 
0.3%
88 20
 
0.3%
4258 20
 
0.3%
Other values (1559) 6741
95.7%
ValueCountFrequency (%)
11 5
0.1%
19 4
0.1%
21 9
0.1%
23 3
 
< 0.1%
25 4
0.1%
27 4
0.1%
28 4
0.1%
31 4
0.1%
38 4
0.1%
42 5
0.1%
ValueCountFrequency (%)
105285 5
0.1%
103214 5
0.1%
101215 5
0.1%
98239 5
0.1%
97318 5
0.1%
96267 5
0.1%
93315 5
0.1%
91664 5
0.1%
91188 4
0.1%
90891 4
0.1%

Churn Value
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
0
5174 
1
1869 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Length

2024-01-30T15:02:50.430248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T15:02:50.571761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring characters

ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7043
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring scripts

ValueCountFrequency (%)
Common 7043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

CLTV
Real number (ℝ)

Distinct3438
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4400.2958
Minimum2003
Maximum6500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:50.712747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2296
Q13469
median4527
Q35380.5
95-th percentile6087
Maximum6500
Range4497
Interquartile range (IQR)1911.5

Descriptive statistics

Standard deviation1183.0572
Coefficient of variation (CV)0.26885855
Kurtosis-0.93403248
Mean4400.2958
Median Absolute Deviation (MAD)922
Skewness-0.3116021
Sum30991283
Variance1399624.2
MonotonicityNot monotonic
2024-01-30T15:02:50.838144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5546 8
 
0.1%
5915 7
 
0.1%
5461 7
 
0.1%
4741 7
 
0.1%
5527 7
 
0.1%
4369 7
 
0.1%
2269 7
 
0.1%
5137 7
 
0.1%
5092 7
 
0.1%
4745 7
 
0.1%
Other values (3428) 6972
99.0%
ValueCountFrequency (%)
2003 3
< 0.1%
2004 3
< 0.1%
2006 1
 
< 0.1%
2007 4
0.1%
2008 1
 
< 0.1%
2009 2
< 0.1%
2010 3
< 0.1%
2011 2
< 0.1%
2013 2
< 0.1%
2014 1
 
< 0.1%
ValueCountFrequency (%)
6500 1
 
< 0.1%
6499 2
< 0.1%
6495 1
 
< 0.1%
6494 2
< 0.1%
6492 3
< 0.1%
6491 1
 
< 0.1%
6490 1
 
< 0.1%
6489 1
 
< 0.1%
6488 1
 
< 0.1%
6487 2
< 0.1%

Churn Category
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.3%
Missing5174
Missing (%)73.5%
Memory size55.2 KiB
Competitor
841 
Attitude
314 
Dissatisfaction
303 
Price
211 
Other
200 

Length

Max length15
Median length10
Mean length9.3750669
Min length5

Characters and Unicode

Total characters17522
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor
2nd rowCompetitor
3rd rowCompetitor
4th rowDissatisfaction
5th rowPrice

Common Values

ValueCountFrequency (%)
Competitor 841
 
11.9%
Attitude 314
 
4.5%
Dissatisfaction 303
 
4.3%
Price 211
 
3.0%
Other 200
 
2.8%
(Missing) 5174
73.5%

Length

2024-01-30T15:02:50.948346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T15:02:51.073805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
competitor 841
45.0%
attitude 314
 
16.8%
dissatisfaction 303
 
16.2%
price 211
 
11.3%
other 200
 
10.7%

Most occurring characters

ValueCountFrequency (%)
t 3430
19.6%
i 2275
13.0%
o 1985
11.3%
e 1566
8.9%
r 1252
 
7.1%
s 909
 
5.2%
C 841
 
4.8%
m 841
 
4.8%
p 841
 
4.8%
a 606
 
3.5%
Other values (10) 2976
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15653
89.3%
Uppercase Letter 1869
 
10.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3430
21.9%
i 2275
14.5%
o 1985
12.7%
e 1566
10.0%
r 1252
 
8.0%
s 909
 
5.8%
m 841
 
5.4%
p 841
 
5.4%
a 606
 
3.9%
c 514
 
3.3%
Other values (5) 1434
9.2%
Uppercase Letter
ValueCountFrequency (%)
C 841
45.0%
A 314
 
16.8%
D 303
 
16.2%
P 211
 
11.3%
O 200
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 17522
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3430
19.6%
i 2275
13.0%
o 1985
11.3%
e 1566
8.9%
r 1252
 
7.1%
s 909
 
5.2%
C 841
 
4.8%
m 841
 
4.8%
p 841
 
4.8%
a 606
 
3.5%
Other values (10) 2976
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3430
19.6%
i 2275
13.0%
o 1985
11.3%
e 1566
8.9%
r 1252
 
7.1%
s 909
 
5.2%
C 841
 
4.8%
m 841
 
4.8%
p 841
 
4.8%
a 606
 
3.5%
Other values (10) 2976
17.0%

Churn Reason
Categorical

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)1.1%
Missing5174
Missing (%)73.5%
Memory size55.2 KiB
Competitor had better devices
313 
Competitor made better offer
311 
Attitude of support person
220 
Don't know
130 
Competitor offered more data
117 
Other values (15)
778 

Length

Max length41
Median length32
Mean length25.256822
Min length5

Characters and Unicode

Total characters47205
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor offered more data
2nd rowCompetitor made better offer
3rd rowCompetitor made better offer
4th rowLimited range of services
5th rowExtra data charges

Common Values

ValueCountFrequency (%)
Competitor had better devices 313
 
4.4%
Competitor made better offer 311
 
4.4%
Attitude of support person 220
 
3.1%
Don't know 130
 
1.8%
Competitor offered more data 117
 
1.7%
Competitor offered higher download speeds 100
 
1.4%
Attitude of service provider 94
 
1.3%
Price too high 78
 
1.1%
Product dissatisfaction 77
 
1.1%
Network reliability 72
 
1.0%
Other values (10) 357
 
5.1%
(Missing) 5174
73.5%

Length

2024-01-30T15:02:51.230478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
competitor 841
 
12.6%
better 624
 
9.4%
of 453
 
6.8%
attitude 314
 
4.7%
had 313
 
4.7%
devices 313
 
4.7%
made 311
 
4.7%
offer 311
 
4.7%
support 263
 
4.0%
person 220
 
3.3%
Other values (37) 2694
40.5%

Most occurring characters

ValueCountFrequency (%)
e 6138
13.0%
t 5212
11.0%
4788
10.1%
o 4650
 
9.9%
r 3698
 
7.8%
i 2918
 
6.2%
d 2538
 
5.4%
s 1917
 
4.1%
p 1896
 
4.0%
a 1816
 
3.8%
Other values (27) 11634
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40330
85.4%
Space Separator 4788
 
10.1%
Uppercase Letter 1898
 
4.0%
Other Punctuation 160
 
0.3%
Dash Punctuation 29
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6138
15.2%
t 5212
12.9%
o 4650
11.5%
r 3698
9.2%
i 2918
 
7.2%
d 2538
 
6.3%
s 1917
 
4.8%
p 1896
 
4.7%
a 1816
 
4.5%
f 1738
 
4.3%
Other values (13) 7809
19.4%
Uppercase Letter
ValueCountFrequency (%)
C 841
44.3%
A 314
 
16.5%
P 198
 
10.4%
L 160
 
8.4%
D 136
 
7.2%
N 72
 
3.8%
S 63
 
3.3%
M 46
 
2.4%
E 39
 
2.1%
W 29
 
1.5%
Other Punctuation
ValueCountFrequency (%)
' 130
81.2%
/ 30
 
18.8%
Space Separator
ValueCountFrequency (%)
4788
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42228
89.5%
Common 4977
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6138
14.5%
t 5212
12.3%
o 4650
11.0%
r 3698
 
8.8%
i 2918
 
6.9%
d 2538
 
6.0%
s 1917
 
4.5%
p 1896
 
4.5%
a 1816
 
4.3%
f 1738
 
4.1%
Other values (23) 9707
23.0%
Common
ValueCountFrequency (%)
4788
96.2%
' 130
 
2.6%
/ 30
 
0.6%
- 29
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6138
13.0%
t 5212
11.0%
4788
10.1%
o 4650
 
9.9%
r 3698
 
7.8%
i 2918
 
6.2%
d 2538
 
5.4%
s 1917
 
4.1%
p 1896
 
4.0%
a 1816
 
3.8%
Other values (27) 11634
24.6%

Total Customer Svc Requests
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3387761
Minimum0
Maximum9
Zeros2443
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:51.346780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4304713
Coefficient of variation (CV)1.0684919
Kurtosis2.1658516
Mean1.3387761
Median Absolute Deviation (MAD)1
Skewness1.3592222
Sum9429
Variance2.0462483
MonotonicityNot monotonic
2024-01-30T15:02:51.450433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 2443
34.7%
1 2012
28.6%
2 1387
19.7%
3 599
 
8.5%
4 336
 
4.8%
5 155
 
2.2%
6 68
 
1.0%
7 28
 
0.4%
8 12
 
0.2%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 2443
34.7%
1 2012
28.6%
2 1387
19.7%
3 599
 
8.5%
4 336
 
4.8%
5 155
 
2.2%
6 68
 
1.0%
7 28
 
0.4%
8 12
 
0.2%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 12
 
0.2%
7 28
 
0.4%
6 68
 
1.0%
5 155
 
2.2%
4 336
 
4.8%
3 599
 
8.5%
2 1387
19.7%
1 2012
28.6%
0 2443
34.7%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30810734
Minimum0
Maximum6
Zeros5621
Zeros (%)79.8%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-01-30T15:02:51.528542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.717514
Coefficient of variation (CV)2.3287793
Kurtosis10.120143
Mean0.30810734
Median Absolute Deviation (MAD)0
Skewness2.906686
Sum2170
Variance0.51482634
MonotonicityNot monotonic
2024-01-30T15:02:51.626793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 5621
79.8%
1 897
 
12.7%
2 373
 
5.3%
3 97
 
1.4%
4 43
 
0.6%
5 8
 
0.1%
6 4
 
0.1%
ValueCountFrequency (%)
0 5621
79.8%
1 897
 
12.7%
2 373
 
5.3%
3 97
 
1.4%
4 43
 
0.6%
5 8
 
0.1%
6 4
 
0.1%
ValueCountFrequency (%)
6 4
 
0.1%
5 8
 
0.1%
4 43
 
0.6%
3 97
 
1.4%
2 373
 
5.3%
1 897
 
12.7%
0 5621
79.8%

Customer Satisfaction
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.3%
Missing5209
Missing (%)74.0%
Memory size55.2 KiB
3.0
675 
4.0
380 
1.0
332 
5.0
247 
2.0
200 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5502
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 675
 
9.6%
4.0 380
 
5.4%
1.0 332
 
4.7%
5.0 247
 
3.5%
2.0 200
 
2.8%
(Missing) 5209
74.0%

Length

2024-01-30T15:02:51.743010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T15:02:51.895684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 675
36.8%
4.0 380
20.7%
1.0 332
18.1%
5.0 247
 
13.5%
2.0 200
 
10.9%

Most occurring characters

ValueCountFrequency (%)
. 1834
33.3%
0 1834
33.3%
3 675
 
12.3%
4 380
 
6.9%
1 332
 
6.0%
5 247
 
4.5%
2 200
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3668
66.7%
Other Punctuation 1834
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1834
50.0%
3 675
 
18.4%
4 380
 
10.4%
1 332
 
9.1%
5 247
 
6.7%
2 200
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 1834
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5502
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1834
33.3%
0 1834
33.3%
3 675
 
12.3%
4 380
 
6.9%
1 332
 
6.0%
5 247
 
4.5%
2 200
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1834
33.3%
0 1834
33.3%
3 675
 
12.3%
4 380
 
6.9%
1 332
 
6.0%
5 247
 
4.5%
2 200
 
3.6%

Interactions

2024-01-30T15:02:37.367196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:01:58.130230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:00.267868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:02.647563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:04.709499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:06.898360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:09.283005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:11.702869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:13.956195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:16.146137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:18.691486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:20.969609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:23.202437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:25.846082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:28.131160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:30.523841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:32.912471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:35.092496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:37.516276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:01:58.265965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:00.384922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:02.747543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:04.828223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:07.015939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:09.430786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:11.832727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:14.076404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:16.297963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:18.803739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:21.081098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:23.349674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:25.995190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:28.248030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:30.657074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:33.021672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:35.201769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:37.649672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:01:58.378248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:00.502200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:02.872362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:04.950508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:07.135736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:09.601408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:11.975177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:14.202777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:16.435553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:18.980483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:21.190892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:23.506393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:26.144564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:28.370347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:30.792548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:33.145810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:35.327588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:37.786023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:01:58.493699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:00.613231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:02.971894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:05.067186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:07.250800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:09.795418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:12.091938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:14.318452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-01-30T15:02:25.213119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:27.634236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:29.839838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:32.432947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:34.653437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:36.815569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:39.526779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:01:59.889065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:02.301428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:04.364064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:06.414392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:08.836927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:11.347512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:13.618067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:15.718750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:18.117624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:20.660830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:22.850881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:25.353497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:27.743804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:29.949583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:32.557387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:34.763252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:36.961042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:39.662067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:00.004136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:02.411403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:04.480880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:06.681252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:08.976480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:11.459963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:13.735654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:15.823562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:18.230617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:20.750058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:22.968119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:25.540831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:27.848368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:30.263278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:32.666315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:34.873002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:37.109143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:39.794482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:00.121490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:02.513158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:04.590865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:06.787694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:09.099559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:11.575604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:13.833376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:15.979937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:18.576764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:20.859867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:23.081480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:25.690101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:27.990082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:30.373016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:32.781132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:34.982749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-01-30T15:02:37.230317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-01-30T15:02:52.067901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Number of ReferralsTenure in MonthsAvg Monthly Long Distance ChargesAvg Monthly GB DownloadMonthly ChargeTotal Regular ChargesTotal RefundsTotal Extra Data ChargesTotal Long Distance ChargesAgeNumber of DependentsZip CodeLatitudeLongitudePopulationCLTVTotal Customer Svc RequestsProduct/Service Issues ReportedReferred a FriendOfferPhone ServiceMultiple LinesInternet ServiceInternet TypeOnline SecurityOnline BackupDevice Protection PlanPremium Tech SupportStreaming TVStreaming MoviesStreaming MusicUnlimited DataContractPaperless BillingPayment MethodGenderUnder 30Senior CitizenMarriedDependentsChurn ValueChurn CategoryChurn ReasonCustomer Satisfaction
Number of Referrals1.0000.3830.0080.0270.0690.3270.0370.0670.257-0.0160.3560.0060.011-0.002-0.0210.131-0.104-0.0900.7180.1060.0000.0750.0420.0580.1460.1130.1270.1200.0710.0560.0530.0330.2210.0530.0540.0000.0080.0200.6820.3210.3170.0270.0750.153
Tenure in Months0.3831.0000.0140.0350.2580.8890.0840.2170.6630.0120.1350.0100.013-0.012-0.0190.367-0.184-0.1610.3590.5620.0000.3330.0210.0470.3260.3580.3590.3250.2770.2830.2340.1110.4970.0000.0950.0000.0000.0230.3780.1320.3640.0000.0180.193
Avg Monthly Long Distance Charges0.0080.0141.000-0.0480.1410.059-0.013-0.0170.651-0.012-0.0060.0060.006-0.005-0.0170.0240.0120.0000.0000.0000.7180.2070.1110.0690.0570.0350.0430.0720.0140.0000.0030.0440.0260.0000.0230.0320.0440.0230.0000.0000.0100.0340.0050.005
Avg Monthly GB Download0.0270.035-0.0481.0000.4990.2800.0150.392-0.037-0.2370.019-0.019-0.0380.0380.0270.0170.0870.0680.0640.0290.1200.1590.7220.4210.2770.2880.2800.2740.2940.3010.3400.3040.1140.2200.1350.0140.6850.2330.0670.1580.2590.0500.0630.115
Monthly Charge0.0690.2580.1410.4991.0000.6220.0350.4160.3050.138-0.143-0.012-0.0330.0310.0120.1010.1010.0960.1340.1180.6600.5260.9670.5600.4090.4760.5080.4280.6610.6560.5450.4030.1940.3630.2220.0000.0560.2350.1410.1750.2780.1010.1100.152
Total Regular Charges0.3270.8890.0590.2800.6221.0000.0870.3840.6500.0670.0420.003-0.0040.006-0.0120.310-0.119-0.1030.3110.3470.1510.4690.4280.2500.4200.5090.5220.4370.5120.5180.4400.2550.3460.1580.1160.0000.0140.1140.3240.0750.2140.0000.0320.129
Total Refunds0.0370.084-0.0130.0150.0350.0871.0000.0520.0610.0200.018-0.004-0.009-0.0090.0330.009-0.012-0.0230.0280.0160.0000.0460.0060.0000.0310.0000.0000.0300.0000.0000.0000.0000.0390.0000.0140.0000.0000.0120.0310.0160.0290.0000.0340.000
Total Extra Data Charges0.0670.217-0.0170.3920.4160.3840.0521.0000.1300.007-0.050-0.014-0.0360.0320.0150.0530.0710.0520.1460.1190.0360.2070.2050.1170.1880.2470.2510.2040.2430.2490.2480.3090.1190.0660.0340.0160.2360.0760.1490.0690.0260.0000.0000.055
Total Long Distance Charges0.2570.6630.651-0.0370.3050.6500.0610.1301.0000.0100.0910.0100.008-0.004-0.0250.240-0.111-0.1040.2570.2580.3410.3330.0450.0330.1980.2430.2100.1880.1840.1870.1490.0510.3210.0190.0720.0270.0110.0220.2690.0950.2460.0000.0310.129
Age-0.0160.012-0.012-0.2370.1380.0670.0200.0070.0101.000-0.120-0.007-0.0080.004-0.0170.0000.0490.0350.0220.0360.0230.1390.1720.0980.0430.0550.0450.0480.0840.1080.1800.0580.0270.1410.0990.0000.9240.9280.0290.1620.1450.0180.0410.093
Number of Dependents0.3560.135-0.0060.019-0.1430.0420.018-0.0500.091-0.1201.0000.0170.029-0.022-0.0260.056-0.102-0.0930.3450.0420.0290.0120.1720.1050.0450.0000.0090.0210.0540.0700.0310.0210.1250.1210.0680.0130.0430.1730.3620.9990.2470.0470.0180.125
Zip Code0.0060.0100.006-0.019-0.0120.003-0.004-0.0140.010-0.0070.0171.0000.880-0.742-0.473-0.002-0.033-0.0030.0250.0510.0270.0310.0430.0230.0120.0220.0000.0100.0000.0000.0140.0000.0240.0040.0490.0140.0000.0000.0270.0230.0620.1490.1740.000
Latitude0.0110.0130.006-0.038-0.033-0.004-0.009-0.0360.008-0.0080.0290.8801.000-0.870-0.478-0.007-0.0320.0070.0310.0320.0000.0150.0600.0370.0200.0320.0000.0000.0000.0000.0000.0000.0290.0220.0520.0000.0230.0220.0250.0250.0970.1520.1980.025
Longitude-0.002-0.012-0.0050.0380.0310.006-0.0090.032-0.0040.004-0.022-0.742-0.8701.0000.3050.0020.026-0.0110.0270.0350.0000.0050.0460.0210.0000.0000.0100.0160.0000.0130.0000.0000.0180.0270.0500.0000.0000.0000.0210.0340.0750.1200.1610.000
Population-0.021-0.019-0.0170.0270.012-0.0120.0330.015-0.025-0.017-0.026-0.473-0.4780.3051.000-0.0080.037-0.0020.0490.0240.0090.0000.0000.0160.0100.0000.0120.0190.0000.0000.0000.0190.0300.0200.0190.0320.0360.0210.0500.0440.0840.0970.1310.039
CLTV0.1310.3670.0240.0170.1010.3100.0090.0530.2400.0000.056-0.002-0.0070.002-0.0081.000-0.058-0.0480.1540.1450.0250.1460.0000.0200.1550.1510.1390.1380.1090.1240.1100.0470.2060.0150.0390.0370.0130.0000.1650.0620.1410.0450.0580.088
Total Customer Svc Requests-0.104-0.1840.0120.0870.101-0.119-0.0120.071-0.1110.049-0.102-0.033-0.0320.0260.037-0.0581.0000.3910.0980.0680.0000.0200.1250.1130.0790.0380.0500.0860.0250.0060.0230.0820.1870.1050.0910.0000.0000.0880.0990.1320.5560.0800.0800.232
Product/Service Issues Reported-0.090-0.1610.0000.0680.096-0.103-0.0230.052-0.1040.035-0.093-0.0030.007-0.011-0.002-0.0480.3911.0000.0630.0470.0420.0220.1040.0940.1040.0200.0000.0720.0410.0290.0310.0480.1350.0850.0770.0150.0210.0640.0590.0950.4060.1530.2340.215
Referred a Friend0.7180.3590.0000.0640.1340.3110.0280.1460.2570.0220.3450.0250.0310.0270.0490.1540.0980.0631.0000.2460.0090.1320.0000.0490.1390.1420.1530.1210.1190.1140.0890.0300.2730.0000.0580.0000.0110.0000.9500.3460.1480.0460.0600.170
Offer0.1060.5620.0000.0290.1180.3470.0160.1190.2580.0360.0420.0510.0320.0350.0240.1450.0680.0470.2461.0000.0100.2370.0460.0420.2340.2500.2460.2240.1910.1910.1610.0780.3390.0000.0680.0200.0000.0650.2560.0900.2600.0410.0470.121
Phone Service0.0000.0000.7180.1200.6600.1510.0000.0360.3410.0230.0290.0270.0000.0000.0090.0250.0000.0420.0090.0101.0000.2790.1710.1730.0920.0500.0700.0950.0190.0300.0370.0880.0000.0110.0240.0000.0000.0000.0120.0000.0000.0200.0590.069
Multiple Lines0.0750.3330.2070.1590.5260.4690.0460.2070.3330.1390.0120.0310.0150.0050.0000.1460.0200.0220.1320.2370.2791.0000.2100.2110.0970.2020.2000.1000.2570.2580.1930.0810.1210.1630.1520.0000.0330.1420.1410.0240.0380.0450.1050.045
Internet Service0.0420.0210.1110.7220.9670.4280.0060.2050.0450.1720.1720.0430.0600.0460.0000.0000.1250.1040.0000.0460.1710.2101.0001.0000.3330.3810.3800.3360.4150.4180.3880.4150.2020.3200.2730.0000.0330.1820.0000.1710.2270.2360.3850.277
Internet Type0.0580.0470.0690.4210.5600.2500.0000.1170.0330.0980.1050.0230.0370.0210.0160.0200.1130.0940.0490.0420.1730.2111.0001.0000.3410.3850.3810.3420.4150.4180.3880.4200.1600.3230.1960.0000.0360.1860.0470.1820.3510.1340.2210.210
Online Security0.1460.3260.0570.2770.4090.4200.0310.1880.1980.0430.0450.0120.0200.0000.0100.1550.0790.1040.1390.2340.0920.0970.3330.3411.0000.2830.2750.3540.1750.1870.1950.2210.2360.0000.0440.0120.0290.0360.1420.0490.1700.0540.1370.304
Online Backup0.1130.3580.0350.2880.4760.5090.0000.2470.2430.0550.0000.0220.0320.0000.0000.1510.0380.0200.1420.2500.0500.2020.3810.3850.2831.0000.3030.2940.2820.2740.2450.2140.1690.1260.0950.0060.0000.0650.1410.0000.0810.0530.1050.087
Device Protection Plan0.1270.3590.0430.2800.5080.5220.0000.2510.2100.0450.0090.0000.0000.0100.0120.1390.0500.0000.1530.2460.0700.2000.3800.3810.2750.3031.0000.3330.3900.4020.3490.1980.2270.1030.0790.0000.0000.0580.1530.0180.0650.0680.0660.103
Premium Tech Support0.1200.3250.0720.2740.4280.4370.0300.2040.1880.0480.0210.0100.0000.0160.0190.1380.0860.0720.1210.2240.0950.1000.3360.3420.3540.2940.3331.0000.2780.2790.2760.2030.2730.0360.0490.0000.0170.0590.1190.0260.1640.0220.0590.154
Streaming TV0.0710.2770.0140.2940.6610.5120.0000.2430.1840.0840.0540.0000.0000.0000.0000.1090.0250.0410.1190.1910.0190.2570.4150.4150.1750.2820.3900.2781.0000.5330.4550.1870.1160.2230.1810.0000.0110.1040.1240.0560.0620.0380.1040.066
Streaming Movies0.0560.2830.0000.3010.6560.5180.0000.2490.1870.1080.0700.0000.0000.0130.0000.1240.0060.0290.1140.1910.0300.2580.4180.4180.1870.2740.4020.2790.5331.0000.8480.1920.1250.2110.1780.0000.0000.1190.1170.0720.0600.0000.0490.048
Streaming Music0.0530.2340.0030.3400.5450.4400.0000.2480.1490.1800.0310.0140.0000.0000.0000.1100.0230.0310.0890.1610.0370.1930.3880.3880.1950.2450.3490.2760.4550.8481.0000.1730.0880.1660.1340.0000.1230.1470.0880.0340.0440.0150.0590.023
Unlimited Data0.0330.1110.0440.3040.4030.2550.0000.3090.0510.0580.0210.0000.0000.0000.0190.0470.0820.0480.0300.0780.0880.0810.4150.4200.2210.2140.1980.2030.1870.1920.1731.0000.0260.1140.0950.0000.0000.0570.0370.0300.1440.0290.0790.108
Contract0.2210.4970.0260.1140.1940.3460.0390.1190.3210.0270.1250.0240.0290.0180.0300.2060.1870.1350.2730.3390.0000.1210.2020.1600.2360.1690.2270.2730.1160.1250.0880.0261.0000.1500.1170.0000.0000.0290.2810.1740.4530.0390.0790.328
Paperless Billing0.0530.0000.0000.2200.3630.1580.0000.0660.0190.1410.1210.0040.0220.0270.0200.0150.1050.0850.0000.0000.0110.1630.3200.3230.0000.1260.1030.0360.2230.2110.1660.1140.1501.0000.1850.0000.0370.1560.0080.1180.1910.0590.0460.183
Payment Method0.0540.0950.0230.1350.2220.1160.0140.0340.0720.0990.0680.0490.0520.0500.0190.0390.0910.0770.0580.0680.0240.1520.2730.1960.0440.0950.0790.0490.1810.1780.1340.0950.1170.1851.0000.0000.0420.1490.0620.1010.2180.0510.0690.113
Gender0.0000.0000.0320.0140.0000.0000.0000.0160.0270.0000.0130.0140.0000.0000.0320.0370.0000.0150.0000.0200.0000.0000.0000.0000.0120.0060.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
Under 300.0080.0000.0440.6850.0560.0140.0000.2360.0110.9240.0430.0000.0230.0000.0360.0130.0000.0210.0110.0000.0000.0330.0330.0360.0290.0000.0000.0170.0110.0000.1230.0000.0000.0370.0420.0001.0000.2180.0090.0390.0530.0000.0000.071
Senior Citizen0.0200.0230.0230.2330.2350.1140.0120.0760.0220.9280.1730.0000.0220.0000.0210.0000.0880.0640.0000.0650.0000.1420.1820.1860.0360.0650.0580.0590.1040.1190.1470.0570.0290.1560.1490.0000.2181.0000.0110.1740.1500.0000.1110.152
Married0.6820.3780.0000.0670.1410.3240.0310.1490.2690.0290.3620.0270.0250.0210.0500.1650.0990.0590.9500.2560.0120.1410.0000.0470.1420.1410.1530.1190.1240.1170.0880.0370.2810.0080.0620.0000.0090.0111.0000.3630.1500.0070.0330.174
Dependents0.3210.1320.0000.1580.1750.0750.0160.0690.0950.1620.9990.0230.0250.0340.0440.0620.1320.0950.3460.0900.0000.0240.1710.1820.0490.0000.0180.0260.0560.0720.0340.0300.1740.1180.1010.0000.0390.1740.3631.0000.2480.0520.0410.261
Churn Value0.3170.3640.0100.2590.2780.2140.0290.0260.2460.1450.2470.0620.0970.0750.0840.1410.5560.4060.1480.2600.0000.0380.2270.3510.1700.0810.0650.1640.0620.0600.0440.1440.4530.1910.2180.0000.0530.1500.1500.2481.0001.0001.0000.853
Churn Category0.0270.0000.0340.0500.1010.0000.0000.0000.0000.0180.0470.1490.1520.1200.0970.0450.0800.1530.0460.0410.0200.0450.2360.1340.0540.0530.0680.0220.0380.0000.0150.0290.0390.0590.0510.0000.0000.0000.0070.0521.0001.0000.9880.201
Churn Reason0.0750.0180.0050.0630.1100.0320.0340.0000.0310.0410.0180.1740.1980.1610.1310.0580.0800.2340.0600.0470.0590.1050.3850.2210.1370.1050.0660.0590.1040.0490.0590.0790.0790.0460.0690.0000.0000.1110.0330.0411.0000.9881.0000.352
Customer Satisfaction0.1530.1930.0050.1150.1520.1290.0000.0550.1290.0930.1250.0000.0250.0000.0390.0880.2320.2150.1700.1210.0690.0450.2770.2100.3040.0870.1030.1540.0660.0480.0230.1080.3280.1830.1130.0000.0710.1520.1740.2610.8530.2010.3521.000

Missing values

2024-01-30T15:02:40.138733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-30T15:02:41.278098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-30T15:02:42.067086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer IDReferred a FriendNumber of ReferralsTenure in MonthsOfferPhone ServiceAvg Monthly Long Distance ChargesMultiple LinesInternet ServiceInternet TypeAvg Monthly GB DownloadOnline SecurityOnline BackupDevice Protection PlanPremium Tech SupportStreaming TVStreaming MoviesStreaming MusicUnlimited DataContractPaperless BillingPayment MethodMonthly ChargeTotal Regular ChargesTotal RefundsTotal Extra Data ChargesTotal Long Distance ChargesGenderAgeUnder 30Senior CitizenMarriedDependentsNumber of DependentsCityZip CodeLatitudeLongitudePopulationChurn ValueCLTVChurn CategoryChurn ReasonTotal Customer Svc RequestsProduct/Service Issues ReportedCustomer Satisfaction
08779-QRDMVNo01NoneNo0.00NoYesFiber Optic9NoNoYesNoNoYesNoNoMonth-to-MonthYesBank Withdrawal41.23639.650.000.00.00Male78NoYesNoNo0Los Angeles9002234.023810-118.1565826870115433CompetitorCompetitor offered more data50NaN
17495-OOKFYYes18Offer EYes48.85YesYesCable19NoYesNoNoNoNoNoNoMonth-to-MonthYesCredit Card83.876633.300.00120.0390.80Female74NoYesYesYes1Los Angeles9006334.044271-118.1852375566815302CompetitorCompetitor made better offer50NaN
21658-BYGOYNo018Offer DYes11.33YesYesFiber Optic57NoNoNoNoYesYesYesYesMonth-to-MonthYesBank Withdrawal99.2681752.5545.610.0203.94Male71NoYesNoYes3Los Angeles9006534.108833-118.2297154753413179CompetitorCompetitor made better offer10NaN
34598-XLKNJYes125Offer CYes19.76NoYesFiber Optic13NoYesYesNoYesYesNoNoMonth-to-MonthYesBank Withdrawal102.4402514.5013.43327.0494.00Female78NoYesYesYes1Inglewood9030333.936291-118.3326392777815337DissatisfactionLimited range of services112.0
44846-WHAFZYes137Offer CYes6.33YesYesCable15NoNoNoNoNoNoNoNoMonth-to-MonthYesBank Withdrawal79.5602868.150.00430.0234.21Female80NoYesYesYes1Whittier9060233.972119-118.0201882626512793PriceExtra data charges102.0
54412-YLTKFNo027Offer CYes3.33YesYesFiber Optic20NoNoYesNoNoNoNoNoMonth-to-MonthYesBank Withdrawal81.1722135.500.00427.089.91Female72NoYesNoYes1Pico Rivera9066033.989524-118.0892996328814638CompetitorCompetitor had better devices00NaN
60390-DCFDQYes11Offer EYes15.28NoYesCable33NoNoNoNoNoNoNoNoMonth-to-MonthYesMailed Check73.26870.450.000.015.28Female76NoYesYesYes2Los Alamitos9072033.794990-118.0655912134313964OtherDon't know70NaN
73445-HXXGFYes658Offer BNo0.00NoYesCable26NoYesYesNoNoYesNoNoMonth-to-MonthYesBank Withdrawal47.1122651.2040.95689.00.00Male66NoYesYesNo0Sierra Madre9102434.168686-118.0575051055815444DissatisfactionService dissatisfaction211.0
82656-FMOKZNo015Offer DYes44.07YesYesCable21NoNoNoNoNoNoNoNoMonth-to-MonthYesMailed Check77.4281145.700.00241.0661.05Female70NoYesNoYes2Pasadena9110634.139402-118.1286582374215717DissatisfactionLimited range of services302.0
92070-FNEXENo07Offer EYes26.95NoYesDSL20YesNoNoNoNoNoNoNoMonth-to-MonthNoBank Withdrawal79.508503.6011.05101.0188.65Female77NoYesNoYes2Pasadena9110734.159007-118.0873533236914419PriceLack of affordable download/upload speed30NaN
Customer IDReferred a FriendNumber of ReferralsTenure in MonthsOfferPhone ServiceAvg Monthly Long Distance ChargesMultiple LinesInternet ServiceInternet TypeAvg Monthly GB DownloadOnline SecurityOnline BackupDevice Protection PlanPremium Tech SupportStreaming TVStreaming MoviesStreaming MusicUnlimited DataContractPaperless BillingPayment MethodMonthly ChargeTotal Regular ChargesTotal RefundsTotal Extra Data ChargesTotal Long Distance ChargesGenderAgeUnder 30Senior CitizenMarriedDependentsNumber of DependentsCityZip CodeLatitudeLongitudePopulationChurn ValueCLTVChurn CategoryChurn ReasonTotal Customer Svc RequestsProduct/Service Issues ReportedCustomer Satisfaction
70339281-CEDRUYes268NoneYes8.62NoYesCable53NoYesNoYesYesNoNoYesTwo YearNoBank Withdrawal64.104326.2519.12229.29586.16Female23YesNoYesNo0Salton City9227533.281560-115.95554179905553NaNNaN10NaN
70340871-OPBXWNo02Offer EYes6.85NoNoNone0NoNoNoNoNoNoNoNoMonth-to-MonthYesMailed Check20.0539.250.000.0013.70Female57NoNoNoNo0Escondido9202733.141265-116.9672214869005191NaNNaN103.0
70359767-FFLEMNo038NoneYes35.04NoYesFiber Optic2NoNoNoNoNoNoNoNoMonth-to-MonthYesCredit Card69.502625.2520.1953.001331.52Male63NoNoNoNo0Westmorland9228133.036790-115.605030238804591NaNNaN10NaN
70368456-QDAVCNo019NoneYes29.55NoYesFiber Optic13NoNoNoNoYesNoNoNoMonth-to-MonthYesBank Withdrawal78.701495.1026.84194.00561.45Male57NoNoNoNo0Winterhaven9228332.852947-114.850784366302464NaNNaN00NaN
70377750-EYXWZNo012NoneNo0.00NoYesFiber Optic24NoYesYesYesYesYesYesYesOne YearNoBank Withdrawal60.65743.3040.4117.840.00Female62NoNoNoNo0Yucca Valley9228434.159534-116.4259842048603740NaNNaN00NaN
70382569-WGERONo072NoneYes22.77NoNoNone0NoNoNoNoNoNoNoNoTwo YearYesBank Withdrawal21.151419.4019.310.001639.44Female30NoNoNoNo0Landers9228534.341737-116.539416218205306NaNNaN00NaN
70396840-RESVBYes124Offer CYes36.05YesYesDSL24YesNoYesYesYesYesYesYesOne YearYesMailed Check84.801990.5048.230.00865.20Male38NoNoYesYes2Adelanto9230134.667815-117.5361831898002140NaNNaN20NaN
70402234-XADUHYes472NoneYes29.66YesYesCable59NoYesYesNoYesYesYesNoOne YearYesCredit Card103.207362.9045.384344.002135.52Female30NoNoYesYes2Amboy9230434.559882-115.6371644205560NaNNaN204.0
70414801-JZAZLYes111NoneNo0.00NoYesDSL17YesNoNoNoNoNoNoYesMonth-to-MonthYesBank Withdrawal29.60346.4527.240.000.00Female32NoNoYesYes2Angelus Oaks9230534.167800-116.86433030102793NaNNaN00NaN
70423186-AJIEKNo066NoneYes30.96NoYesFiber Optic11YesNoYesYesYesYesYesYesTwo YearYesBank Withdrawal105.656844.500.000.002043.36Male44NoNoNoNo0Apple Valley9230834.424926-117.1845032881905097NaNNaN12NaN